CODEWITHSUNDEEP
nlpsentiment-analysistext-processingtextblobvader
eeveepotato
eeveepotato

Reputation: 13

Transcript transformation for sentiment analysis

I'm doing sentiment analysis on users' transcripts for UX website testing. I get the transcript from the testing session and then I analyze the transcript for sentiment analysis - what's the user's opinion about the website, what problems did the user encounter, whether he had any problems, got stuck, lost. Since this is quite domain-specific, I'm testing both TextBlob and Vader and see which gives better results. My issue is at the beginning of the process - the speech-to-text API's transcript isn't perfect. Sentences (periods) are not captured or are minimal. I'm not sure on what level the analysis should be since I was hoping I could do it on sentence-level. I tried making n-grams and analyzing those short chunks of text, but it isn't ideal and the results are slightly difficult to read - because there will be some parts that are repeated. Apart from this, I do classical text cleaning, tokenization, pos tagging, lemmatization and feed it to TextBlob and Vader.

Transcript example: okay so if I go just back over here it has all the information I need it seems like which is great so I'm pretty impressed with it similar to how a lot of government websites are set up over here it looks like I have found all the information I need it's a great website it has everything overall though it had more than enough information...

I did:

ngram_object = TextBlob(lines)
ngrams = ngram_object.ngrams(n=4)

which gives me something like (actually a WordList): [okay so if I, so if I go, if I go just...]

Then the results look like:

62  little bit small    -0.21875    Negative  
61  like little bit     -0.18750    Negative

0 information hard find not see -0.291666667    Negative
1 hard find not see information -0.291666667    Negative

Is there a better way to analyze unstructured text in chunks rather than a full transcript?

This makes it difficult to capture what was the issue with the website. Changing the API isn't really an option since I'm working with something that was given to me to use as data collection for this particular sentiment analysis problem.

Any tips or suggestions would be highly appreciated, couldn't find anyone doing something similar to this.

Upvotes: 0

Views: 218

Answers (1)

Thibault Roux
Thibault Roux

Reputation: 1

I am not sure about what you really want but maybe you could take a look on speech sentiment analysis? I have read about RAVDESS, a database useful for sentiment classification. Take a look: https://smartlaboratory.org/ravdess/

Upvotes: 0

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